In the previous section, we have presented various neural networks. This section highlights the role of CNN in fighting against infectious diseases; especially, COVID-19. Since the COVID-19 outbreak in China in December 2019, it has ruthlessly affected the global population and the global economy. The most crucial part of alleviating the spread of COVID-19 is its timely detection, as it is significantly essential to separate and quarantine the infected patient. The core screening technique used to detect COVID-19 is polymerase chain reaction (PCR) testing; however, it is costly and short in supply. Therefore, an alternative screening method such as radiography examination is used to detect COVID-19, which indicates that people affected with COVID-19 have irregularities in their chest, especially in the lungs. However, a specialist radiologist is required to analyze such radiography images. Therefore, there is an urgent need for an automated system that can analyze radiography images and lessen the workload of radiologists. In this case, neural networks-based X-ray screening is assumed a promising technique to test COVID-19 in asymptomatic patients. The leading interest in developing neural networks-based techniques for COVID-19 detection led to the development of many state-of-the-art neural network models to enhance COVID-19 detection accuracy [1]}, [2]}. Therefore, we provide a comprehensive review of existing neural network models to thoroughly investigate the working of existing models in terms of their achievements and limitations.
